You adopted AI faster
than you could govern it.
Most organizations cannot answer basic questions about their own AI: what is running, who approved it, what data it touches, what it costs. VynVault is the system of record that answers them.
Unmonitored reads not measured, never zero.
AI adoption is accelerating. Governance is not.
Every enterprise is becoming an AI enterprise. Employees are using copilots. Business teams are deploying AI applications. Developers are building agents. Vendors are embedding AI into products you already bought.
None of that waited for a governance program to exist.
Employees
Copilots and GenAI tools adopted individually, often on a corporate card.
Business teams
AI applications deployed to solve a local problem, outside any review.
Developers
Agents wired into production systems with real permissions.
Vendors
AI embedded into SaaS you already own, arriving through a release note.
Six questions your board will ask.
Most organizations cannot answer four of them.
Do we know where AI is being used?
Not the tools you procured. The ones a team signed up for with a corporate card, the copilot embedded in a SaaS product you already own, the agent a developer wired into production last quarter.
Who approved it, and on what basis?
Approval that lives in an email thread is not approval. When a regulator asks who accepted the risk, the answer needs a name, a date and a stated rationale.
Are we ready for the regulation that has not arrived yet?
The EU AI Act is phasing in. ISO 42001 is being asked for in tenders. The next framework is already being drafted. Building a compliance program around one regulation means rebuilding it for the next.
Can we prove any of it?
An assessment score with no evidence behind it is an opinion. The question is not whether you assessed a system, but whether you can show what the assessment was based on and reproduce it a year later.
What is it costing us?
Token spend accumulates across teams, vendors and models with no single owner. The first accurate number many organizations see is the annual invoice.
What happens when something goes wrong?
A model drifts, a prompt leaks personal data, a vendor changes its terms. The question is whether that reaches the right person before it reaches your customers.
If you hesitated on more than two, the problem is not that you lack a policy. It is that governance lives in documents, and AI does not.
One AI estate. One governance model. One evidence trail.
Governance fails in the gaps between records. An inventory that does not know which vendor a system depends on, a risk that does not know which control treats it, an assessment that cannot point at the evidence behind its own score.
VynVault holds these as one connected model rather than a set of separate registers.
When these relationships are connected, governance stops being a collection of assessments and becomes an operating model for enterprise AI.
You can answer questions without a project.
Which systems process personal data and have no assigned owner? That is a filter, not a two-week exercise.
Approval is a path, not an email.
A request enters a workflow, reaches the right approver, and leaves a record of who decided what and why.
Evidence is a by-product of working, not a task before an audit.
Because assessments run against live data, the evidence already exists.
Govern every AI you build, buy, or use.
Most governance tooling assumes AI is something you build. In practice, three quite different things arrive through three quite different doors, and only one of them passes through engineering.
Internal models, applications and agents
Design review, evaluation harnesses, guardrails, and the change control that keeps them true after launch.
SaaS platforms, AI APIs and foundation models
Vendor assessment, contract and certification records, and the approval path that cleared them.
Employee copilots, GenAI tools and embedded AI
Discovery of what is already in use, acceptable-use policy, attestation, and data-exposure review.
Govern AI from idea to retirement.
Governance that only happens at assessment time governs a moment. These are the seven points at which a decision actually gets made.
Four centers. One operating model.
Govern AI throughout its lifecycle, not just during an assessment.
Every capability, tied
to the question it answers.
Know what you have
AI Inventory Management
Every AI system with its lifecycle stage, risk tier, data classification, business owner and technical owner. Fields are configurable, so the register matches how your organization actually describes AI.
AI Agent Management
Autonomous agents held separately, because the governance questions differ: what can it act on, what tools can it call, and how much human oversight sits between it and a customer.
AI Vendor Management
Third-party AI as a first-class record, with contract dates, certifications and the assessments that cleared it.
AI Governance Graph
The relationship view: which systems depend on which vendors, which policies constrain which agents, which risks attach to which owners. Governance failures usually happen in the gaps between records, not inside them.
Control what gets in
AI Onboarding and Approval Management
A request for a new AI tool follows a configured path: business review, risk owner, privacy, security, each with its own SLA and escalation. The stages are configurable per organization and per department.
Policy Management
Policies as governed records with owners, review dates and attestation, mapped to the controls that implement them.
Approval separated from authorship
A user with permission to create a record does not thereby have permission to approve one. In VynVault those are distinct rights, so a preparer cannot sign off their own submission.
Prove you are compliant
Framework-agnostic by design
ISO 42001, the EU AI Act, NIST AI RMF, GDPR, SOC 2 and ISO 27001 are configuration, not code. A new framework is added as content: controls, mappings and questions. Nothing is rebuilt.
Compliance and Controls Management
Baseline controls mapped across frameworks, so a control satisfied once is credited everywhere it applies rather than assessed repeatedly.
Assessments that explain themselves
Every assessment returns a score plus its coverage, confidence, freshness and the evidence behind each finding. Rules are versioned, so an assessment run last year can be reproduced exactly as it stood.
Three distinct assessment types, deliberately not merged
Compliance assessment audits against a framework, impact assessment evaluates a single system for DPIA or FRIA, readiness assessment measures organizational preparedness. Conflating them loses the regulatory distinction.
Manage what goes wrong
AI Risk Management
Risks with likelihood, impact, owner and treatment plan, following an approval path rather than sitting in a spreadsheet column.
AI Incident Management
Incidents with severity, affected systems and regulatory reportability, escalating automatically where configured.
Validation, Exceptions and Decisions as first-class records
An exception cannot be submitted without a justification and compensating controls, or approved without an expiry date. The decision register is append-only: a decision is superseded, never edited, so the record shows what was believed at the time.
Continuous monitoring
Thresholds on live telemetry can trigger a reassessment, a notification, a task, or a draft decision for a human to complete. It never records a governance decision on your behalf.
Know what it costs
AI FinOps
Vendor spend, model cost, budgets, utilization and threshold alerts, with an accountable budget owner required on every vendor.
Cost tied to connectivity
Vendors connected through the platform feed their own usage, so spend reconciles against the traffic that produced it rather than a name someone retyped.
Cost decisions follow the same approval path
Budget owner confirms the need, finance confirms affordability, then the vendor goes live.
Connect it to what you already run
Integration Framework
A curated connector registry with scheduled sync, retry, provenance and health monitoring. Endpoints are allowlisted per connector, so an integration never accepts an arbitrary URL.
OpenTelemetry connector
AI telemetry using GenAI semantic conventions.
Generic webhook connector
An internal system can push governance data without waiting for a bespoke connector to be built.
Metric mapping
Two vendors reporting drift under different names land on one canonical metric that can be scored and compared.
Single sign-on
OIDC and SAML with IdP group to role mapping and just-in-time provisioning.
API access for third-party systems
Scoped keys: a key that reads the AI register cannot delete a policy. Secrets are shown once and stored as a hash.
A governance platform that invents a number is worse than no platform.
Most tools show you a dashboard full of green. The question nobody asks is where those numbers came from.
VynVault will not display a metric it cannot evidence. A system with no telemetry reads not measured, never zero, so an unmonitored system is never mistaken for a healthy one. A metric past its freshness budget degrades to stale rather than repeating its last known value indefinitely.
How much of the estate actually reported.
How much weight to place on the result.
How recently the underlying data arrived.
Where coverage or confidence is too low to support a board decision, the report says so rather than presenting the number anyway. This is an uncomfortable design choice. It means demos show empty states, and it means a report sometimes tells an executive that the answer is not yet knowable. We think that is the only defensible position for a product whose output is used to make regulatory claims.
From AI chaos to AI control.
One assessment. Multiple obligations. One evidence trail.
AI regulations do not exist independently. The same AI system may need to satisfy overlapping requirements across several frameworks at once, and most of those requirements are asking about the same control.
VynVault connects controls and evidence across frameworks, so a control satisfied once is credited everywhere it applies rather than assessed repeatedly.
Your governance model,
without a development cycle.
Governance vocabulary differs by organization, and a platform that requires a code change to add a field will always lag the business.
An administrator changes these in the product. The change applies immediately, and it is recorded in the audit trail like any other change.
- Forms and their fields, per entity
- Assessment types, questions, scoring rules and thresholds
- Approval workflows: stages, approvers, SLAs, escalation paths
- Business ID formats
- Dashboards and widgets
- Roles and their permissions, per module and operation
- Which modules appear in the menu at all
- Frameworks, controls and control mappings
Access that survives an audit.
Permissions are granted per module and per operation: read, create and edit, approve, delete, export.
Export is separate from read
Viewing data inside the platform is not the same as taking it out of it.
Approve is separate from create
A preparer cannot approve their own work.
No deny rules, deliberately
A user can hold several roles and their access is the union. A deny that silently overrides a grant is the hardest thing to explain when someone asks why they cannot see a record.
Every change to a permission, a record, a configuration or an approval is written to a single audit trail, filterable by section.
What VynVault changes.
Reduce governance effort
Automate assessments, evidence, workflows and reporting instead of assembling them by hand each cycle.
Reduce AI risk
Identify and treat risk while it is still a finding, rather than after it becomes an incident.
Accelerate AI adoption
Create guardrails clear enough that teams can move quickly inside them without asking permission each time.
Improve audit readiness
Maintain continuous evidence rather than preparing manually in the weeks before an audit.
Control AI spend
Connect AI usage and cost to the governance record, with an accountable owner on every vendor.
Increase executive visibility
Give leadership a live view of AI risk and governance posture, qualified so it can be acted on.
Built for the people
accountable for AI.
Chief AI Officer
Know what AI the enterprise is actually running.
CIO and CTO
Scale AI without losing control of the estate.
Chief Risk Officer
Quantify and manage AI risk continuously.
CISO
Understand AI-related security exposure and response.
Legal, Privacy and Compliance
Prove regulatory compliance with defensible evidence.
CFO and Finance
Understand where AI spend goes and what value it returns.
AI Product and Engineering
Build and deploy inside defined guardrails.
Board and Executive Leadership
See enterprise AI governance posture in one place.
AI will move faster than your governance.
Do not slow innovation down. Govern it at the speed of AI.
VynVault gives enterprises the visibility, controls, intelligence and evidence to build, buy and deploy AI with confidence.